
@article{carey_ten_2020,
	title = {Ten simple rules for reading a scientific paper},
	volume = {16},
	issn = {1553-7358},
	url = {https://dx.plos.org/10.1371/journal.pcbi.1008032},
	doi = {10.1371/journal.pcbi.1008032},
	pages = {e1008032},
	number = {7},
	journaltitle = {{PLoS} Comput Biol},
	author = {Carey, Maureen A. and Steiner, Kevin L. and Petri, William A.},
	editor = {Markel, Scott},
	urldate = {2024-03-01},
	date = {2020-07-30},
	langid = {english},
	file = {file:D\:\\document\\storage\\MAAL89AK\\file:application/pdf},
}

@article{liu_joint_2022,
	title = {Joint dimension reduction and clustering analysis of single-cell {RNA}-seq and spatial transcriptomics data},
	volume = {50},
	issn = {0305-1048, 1362-4962},
	url = {https://academic.oup.com/nar/article/50/12/e72/6555431},
	doi = {10.1093/nar/gkac219},
	abstract = {Abstract
            Dimension reduction and (spatial) clustering is usually performed sequentially; however, the low-dimensional embeddings estimated in the dimension-reduction step may not be relevant to the class labels inferred in the clustering step. We therefore developed a computation method, Dimension-Reduction Spatial-Clustering ({DR}-{SC}), that can simultaneously perform dimension reduction and (spatial) clustering within a unified framework. Joint analysis by {DR}-{SC} produces accurate (spatial) clustering results and ensures the effective extraction of biologically informative low-dimensional features. {DR}-{SC} is applicable to spatial clustering in spatial transcriptomics that characterizes the spatial organization of the tissue by segregating it into multiple tissue structures. Here, {DR}-{SC} relies on a latent hidden Markov random field model to encourage the spatial smoothness of the detected spatial cluster boundaries. Underlying {DR}-{SC} is an efficient expectation-maximization algorithm based on an iterative conditional mode. As such, {DR}-{SC} is scalable to large sample sizes and can optimize the spatial smoothness parameter in a data-driven manner. With comprehensive simulations and real data applications, we show that {DR}-{SC} outperforms existing clustering and spatial clustering methods: it extracts more biologically relevant features than conventional dimension reduction methods, improves clustering performance, and offers improved trajectory inference and visualization for downstream trajectory inference analyses.},
	pages = {e72--e72},
	number = {12},
	journaltitle = {Nucleic Acids Research},
	author = {Liu, Wei and Liao, Xu and Yang, Yi and Lin, Huazhen and Yeong, Joe and Zhou, Xiang and Shi, Xingjie and Liu, Jin},
	urldate = {2024-03-04},
	date = {2022-07-08},
	langid = {english},
	file = {Liu et al_2022_Joint dimension reduction and clustering analysis of single-cell RNA-seq and.pdf:D\:\\document\\storage\\HATZ3WTE\\Liu et al_2022_Joint dimension reduction and clustering analysis of single-cell RNA-seq and.pdf:application/pdf},
}
